This is a proposal to apply systems biology approaches to evaluate how quantitative variation during signal transduction affects patterning during embryogenesis and wing patterning in the model organism, Drosophila. We will precisely quantify gene expression profiles for 1000 genes involved in receptor-mediated signaling during wing development in 210 wild-type lines and 25 mutant backgrounds, and associate SNP promoter polymorphism in 25 key genes with both transcript abundance and phenotypic variation. The patterns of covariance at the three levels (genotype, gene expression, and phenotype) in combination with knowledge of the biochemical networks connecting the components will be used to generate predictive models of how variation in individual components is either buffered by the other components or directs development into a particular trajectory. Genetic and environmental perturbations will then be introduced to test specific predictions, and to differentiate causality from correlation in the complex network of interactions. The biomedical significance of this proposal lies first in the generation of a rich data set connecting most of the known regulatory components in Drosophila development (this data set will be similar to data sets now planned for a wide variety of complex disease studies); second in the development of statistical methods for dealing with the so-called "curse of multidimensionality" in evaluating associations at the genome scale; and third in utilizing a manipulable genetic system to actually evaluate the functional significance of specific interactions. This research shifts the emphasis in molecular quantitative genetic analysis from isolation of individual SNP effects to identification of the regulatory control points that produce homeostasis in complex regulatory systems. [unreadable] [unreadable]